Being able to compare two or more sets of audio data has many uses. However, techniques that compare audio data suffer from a variety of problems. Some techniques are too computationally intensive to be suitable for applications requiring fast response time or that require a comparison of reference audio data with many other audio data sets. Other techniques trade off accuracy to achieve a relatively fast comparison.
One technique analyzes the audio data to produce a set of numeric values (vector) that can be used to classify and rank the similarity between individual audio files. However, this technique requires a computationally intensive distance comparison between the vector for the two files that are being compared. Moreover, the distance comparison only compares two files at a time.
Another technique generates templates for audio files and compares the templates to determine similarity. However, the comparison involves a computationally intensive distance measurement between two templates to compare two audio files. Moreover, the distance comparison only compares two files at a time.
A different approach involves text based comparisons based on metadata, anchor text, or song lyrics associated with the audio rather than a comparison based on the audio content. For example, audio data may have metadata that describes a song title, artist, genre, lyrics, etc. However, the effectiveness of comparing such metadata depends upon the richness and accuracy of metadata provided. Moreover, this approach faces a problem of whenever audio data is added to the search group, metadata may need to be entered manually. Manual intervention makes the metadata highly subjective, thus reducing comparison accuracy. Also, the textual information can be incomplete, wherein the comparison is not accurate. Further, this approach does not take into account the audio content of the audio data. Thus, audio data that may be very similar may be tagged with very different metadata. Furthermore, similar metadata does not necessarily imply similar audio content. Thus, a need exists for a technique to compare audio data that does not suffer from the limitations of the prior approaches.
The approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section.